Effects of a Government-Academic Partnership: Has the NSF-CENSUS Bureau Research Network Helped Improve the US Statistical System?

Daniel H. Weinberg, John M. Abowd, Robert F. Belli, Noel Cressie, David C. Folch, Scott H. Holan, Margaret C. Levenstein, Kristen M. Olson, Jerome P. Reiter, Matthew D. Shapiro, Jolene D. Smyth, Leen Kiat Soh, Bruce D. Spencer, Seth E. Spielman, Lars Vilhuber, Christopher K. Wikle

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly to the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This article focuses on some of the key research findings of the eight nodes, organized into six topics: (1) improving census and survey data-quality and data collection methods; (2) using alternative sources of data; (3) protecting privacy and confidentiality by improving disclosure avoidance; (4) using spatial and spatio-temporal statistical modeling to improve estimates; (5) assessing data cost and data-quality tradeoffs; and (6) combining information from multiple sources. The article concludes with an evaluation of the ability of the FSS to apply the NCRN's research outcomes, suggests some next steps, and discusses the implications of this research-network model for future federal government research initiatives.

Original languageEnglish (US)
Pages (from-to)589-609
Number of pages21
JournalJournal of Survey Statistics and Methodology
Issue number4
StatePublished - Dec 1 2019
Externally publishedYes


  • Administrative records
  • Data collection
  • Disclosure avoidance
  • Statistical modeling
  • Survey methods
  • Survey statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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